code: def _get_model_output(
      self,
      x,
      t,
      unconditional_conditioning,
      unconditional_guidance_scale,
      score_corrector,
      c,
      corrector_kwargs,
  ):
      if (
          unconditional_conditioning is None
          or unconditional_guidance_scale == 1.0
      ):
          model_output = self.model.apply_model(x, t, c)
      elif isinstance(c, dict):
          ### Contolnet conditioning is dict format
          model_t = self.model.apply_model(x, t, c)
          model_uncond = self.model.apply_model(
              x, t, unconditional_conditioning
          )
          model_output = model_uncond + unconditional_guidance_scale * (
              model_t - model_uncond
          )
      else:
          x_in = torch.cat([x] * 2)
          t_in = torch.cat([t] * 2)
          c_in = torch.cat([unconditional_conditioning, c])
          e_t_uncond, model_t = self.model.apply_model(
              x_in, t_in, c_in
          ).chunk(2)
          model_output = e_t_uncond + unconditional_guidance_scale * (
              model_t - e_t_uncond
          )
      if self.model.parameterization == "v":
          e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
      else:
          e_t = model_output
      if score_corrector is not None:
          assert self.model.parameterization == "eps"
          e_t = score_corrector.modify_score(
              self.model, e_t, x, t, c, **corrector_kwargs
          )
      return e_t, model_output
  
file_path: NeMo/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/base_sampler.py
imports: from abc import ABC, abstractmethod
  import numpy
  import torch
  from tqdm import tqdm
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers import Sampler
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
label_desc: Use image node_id 9 for calling the AbstractBaseSampler obj's underlying member method code with example usage: obj._get_model_output(x, t, unconditional_conditioning, unconditional_guidance_scale, score_corrector, c, corrector_kwargs) and returns: e_t,  model_output